class_docstr: This class implements a residual block.
  
  Arguments
  --------
  dim : int
  Input channels of the tensor to process. Matches output channels of the residual block.
  
  Returns
  --------
  Residual block output : torch.Tensor
  
  Example
  --------
  >>> res = ResBlockAudio(128)
  >>> x = torch.randn(2, 128, 16, 16)
  >>> print(x.shape)
  torch.Size([2, 128, 16, 16])
docstr: Forward step.
  
  Arguments
  --------
  x : torch.Tensor
      Tensor to process. Expected shape is `torch.Size([B, C, H, W])`.
  
  Returns
  --------
  Residual block output : torch.Tensor
docstr_tok: ["Forward","step",".","Arguments","--","--","--","--","x",":","torch.Tensor","Tensor","to","process",".","Expected","shape","is","`","torch.Size","(","[","B",",","C",",","H",",","W","]",")","`",".","Returns","--","--","--","--","Residual","block","output",":","torch.Tensor"]
code: def forward(self, x):
      """Forward step.
  
      Arguments
      --------
      x : torch.Tensor
          Tensor to process. Expected shape is `torch.Size([B, C, H, W])`.
  
      Returns
      --------
      Residual block output : torch.Tensor
      """
      return x + self.block(x)
  
imports: import torch
  import torch.nn
  from torch.autograd import Function
label_desc: Use image node_id 2 for calling the ResBlockAudio obj's underlying member method code with example usage: obj.forward(x) and returns: unknown